An image searching method and apparatus

Abstract: Image searching method and apparatus capable of performing high quality search out of image database by using color information. A sample image which is used as a key to search, and which is made by the operator, is inputted by the sample image input unit 30, and the sample color information extraction unit 50 extracts the HSV color space data out of the sample image, and the image color information extraction unit 40 extracts the HSV color image data from the image database 20. Contribution factors deciding unit 60 decides a contribution factor of each component in the HSV color space, then the candidate image deciding unit 70 calculates similarity, and decides the image candidates, then those images are displayed on the display unit 90. the control unit 80 controls entire image searching apparatus.

Description: The present invention relates to an image processing method and apparatus and, more particularly, to a method to search for an image out of an image database, and the apparatus capable of employing the method. As one of conventional methods to search for a necessary image out of an image database, there is a method that an operator makes an example of the desired image, and that the search is performed with reference to color information of the image. The color information is extracted from the desired image by using a color perception space which is composed of three elements, namely, hue, chromaticity, and luminance (altogether, called `the three attributes of colors', hereinafter). Then, similarity between the extracted color information of the desired image and color information extracted from an image stored in the image database by employing the same color perception space, is examined. Thereby, the order of image candidates, which can be the same as or which can be similar to the desired image made by the operator, is decided based on similarity. The image candidate which has a higher similarity is more likely similar to the desired image. then the images are displayed in order of similarity. Regarding color data, each factor of the three attributes of color differs in accordance with values of the three attributes of the color. For instance, in a color which has low chromaticity that is close to black or white, and low luminance, a contribution factor of hue which is an attribute to distinguish the difference of color, such as red, blue, and yellow, is also low. If the degree of the similarity is calculated on the assumption that the contribution factors of the attributes are the same for all the colors in spite of the aforementioned fact that the contribution factors actually differ, the searching quality becomes lower since low contribution values are included in the calculation of similarity. It is known from EP-A-0558054 to provide image retrieval based on color classification in which images in a database are characterised at the time of entry into the database by the numbers of pixels having colors corresponding to each of a set of color areas in a color space. A search is based on specific user selected colors and proportions thereof. It is also known from JP-A-05158999 to store images in a database together with a retrieval code based on color content and to retrieve images from the database using a code representative of a color sample. There is a need to provide an image processing method and apparatus which make the quality of search by referring to color information higher when an image is searched out of the image database, and to make it easier for an operator to search for the desired image. Aspects of the present invention are set out in the appended claims. According to the present invention there is further disclosed image searching apparatus for retrieving a desired image from a database including color information of stored images, the apparatus comprising: input means operable to input information about the image to be retrieved comprising color information including a plurality of color components in a perceptual color space; generating means operable to generate a sample image using the color information input via said input means; similarity calculation means operable to calculate a respective similarity between the sample image and each image stored in the database based on a calculation of similarity between the color information of the sample image and the color information of each image stored in the database; characterised by further comprising contribution factor calculating means operable to calculate for each of the plurality of color components a respective contribution factor based on values of the plurality of color components of the sample image; contribution rate calculating means operable to calculate a respective contribution rate of each of the plurality of color components by normalizing the calculated contribution factors; and wherein the similarity calculating means is operable to calculate the similarity between the sample image and each image stored in the database taking into account the corresponding contribution rates such that the rate at which each color component of the perceived color space contributes to the similarity calculation depends upon the values of the color components in the sample image. Other features and advantages of the present invention will be apparent from the following description taken in conjunction with the accompanying drawings, in which like reference characters designate the same or similar parts throughout the figures thereof. The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention by way of example only. Fig. 1 is a block diagram showing a block diagram of an image searching apparatus in a first and second embodiments; Fig. 2 is a flowchart of a searching processing sequence; Fig. 3 is a flowchart showing a procedure of making a sample image; Fig. 4 illustrates a structure of a display for designating colors when making of the sample image; Fig. 5 is a flowchart showing a process to determine similarity; Fig. 6 is a flowchart showing a process for calculating each contribution factor of the three attributes of color data; Fig. 7 illustrates a shape of a membership function of luminance; Fig. 8 illustrates a shape of a membership function of chromaticity; Fig. 9 is a flowchart showing a process for calculating the contribution factor using the membership functions; and Fig. 10 is a flowchart showing another process of calculating the contribution factor using the membership functions. Embodiments of the present invention will be described in detail in accordance with the accompanying drawings. (First Embodiment) In the first embodiment, a HSV (H stands for hue, S is for chromaticity, and V is for luminance) color space, for instance, is employed as a color perception space for indicating color data by hue, chromaticity, and luminance. In Fig. 1, reference numeral 10 denotes a stored image data input unit for inputting image data to be stored in database, 20 denotes an image storage unit for storing the image data inputted at the stored image data input unit 10, and 30 denotes a sample image input unit for inputting a sample image made by an operator to obtain the desired image, where the sample image input unit 30 has a pointing input device 31. Further, an image color information extraction unit 40 extracts color information out of the image data stored in the image storage unit 20 by using the HSV color space, a sample color information extraction unit 50 extracts color information out of the sample image inputted at the sample image input unit 30 by using the HSV color space, and a contribution factors deciding unit 60 decides contribution factors of hue H, chromaticity S, and luminance V of color data in the HSV color space. 70 is a candidate image deciding unit to decide the searched candidate images by calculating the similarity, 80 is a control unit to control the entire apparatus in this embodiment, and 90 is a display unit to display the searched candidate images. Fig. 2 is a flowchart showing an image searching process performed by the apparatus in Fig. 1. An image searching process is explained below with reference to Fig. 2. At step S1, a sample image of a desired image is made and inputted by the operator at the sample image input unit 30, and the sample image is transmitted to the control unit 80. The process of making the sample image is explained later in detail with reference to the flowchart in Fig. 3 and the illustration of the display in Fig. 4. At step S2, the sample color information extraction unit 50 extracts the color information, namely, the values of the three attributes in the HSV color space, H1, S1, and V1, out of the inputted sample image. At step S3, the image color information extraction unit 40 finds a mode color in pixel units (the "mode" here is a term in statistics) in a designated portion of each image i (i=1,2,...n: n is a total number of stored images) which is stored in the image storage unit 20, where the designated portion of each image i corresponds to the portion designated by the operator in the sample image, and the pixel mode color is defined as a representative value of the portion. Then each value of three attributes in the HSV color space, H2i, S2i, and V2i, are extracted. It should be noted that in the aforementioned embodiment the representative value of the designated portion is defined by a mode color, however, it can be defined by a mean or a median in statistics, for instance. At step S4, the similarity between the sample image and the stored images are calculated based on two different sets of the extracted color information, that is, (H1, S1, V1) and (H2i, S2i, V2i), and also on each contribution factor decided in the contribution factors deciding unit 60. This process is explained later in detail with reference to Fig. 5. At step S5, the stored images are sorted in order of similarity in accordance with the similarity Ri (i=1,2,3, n). A stored image which has the higher similarity is considered as more similar image to the sample image than the others. Note that the processes 4 and 5 are performed at the candidate image deciding unit 70. At step 6, the sorted images are displayed in order of the degree of the similarity on the display unit 90. Thus, the desired image is searched for from the stored images based on the color information of the sample image made by the operator by following the aforementioned procedure. Next, the sample image input process of step S1 is explained with reference to the flowchart of Fig. 3. The operator makes the sample image in a window which is open on the screen of the display unit 90 in the image processing apparatus by following the procedure shown in Fig. 3. At step S10, the operator designates a portion which represents an object image in the desired image by the pointing input device 31. At step S11, a color is designated in the portion. An example of color designation by using the screen of the display unit 90 is illustrated in Fig. 4. Reference numeral 41 denotes six color chips including the three primary colors (red, green, blue) and the intermediate colors of the three primary colors (yellow, cyan, magenta), 42 denotes slide-bars to adjust each value of the HSV. The operator designates a color which is most similar to the color of portion by using the pointing input device 31. Then the indicated color is considered as standard, and each values of the HSV is adjusted so that the color becomes more similar to the color of the object in the desired image by moving the slide-bars with the pointing input device 31. The color under adjustment is displayed in a box 43 on the screen, so that the operator can see what kind of the color the operator designated. After the adjustment is finished, the color designation is completed by clicking a "quit" display using the pointing input device 31. Thereby the color of the portion of the sample image is decided. At step S12, the portion which is selected at step S10 is filled with the color decided at step S11. At step S13, the operator checks whether or not there is a need to designate another portion, and if not, the process of making the sample image is completed. Further, if another portion needs to be designated, the process returns to step 10, and the same process described above needs to be repeated. Thus, the sample image is made by performing aforementioned steps. Next, the process to obtain the respective similarity between the sample image of step S4 and the stored images is described in detail with reference to the flowchart in Fig. 5. Note that it is assumed that only one portion is designated by the operator. In this process, the similarities calculated based on the color information of the sample image (H1, S1, V1) and on the color information of each stored image (H2i, S2i, V2i, where i=1, 2, 3, ,n: n is a total number of the stored images). At step S30, first, each contribution factor, RH, RS, RV, for normalizing the three attributes of the color information of the sample image, H1, S1, and V1 is decided by using the contribution factors deciding unit 60. The process in the contribution factors deciding unit 60 is described later with reference to Fig. 6. At step S31, the value differences of the three attributes between the color information of the sample image, H1, S1, and V1, and the color information of the stored images, H2i, S2i, and V2i, are calculated. The respective differences are described as, ΔHi(ΔHi=|H1-H2i|), ΔSi(ΔSi=|S1-S2i), and ΔVi(ΔVi=|V1-V2i|), where i=1, 2, 3, ,n: n is the total number of the stored images. At step S32, color differences, in which the contribution factors are considered, DHi(DHi=RH*Δhi), DSi(DSi=RS*ΔSi), and Dvi(Dvi=RV*ΔVi), where i=1, 2, 3, ,n: n is the total number of the stored images, are calculated on the basis of RH, RS, and RV which are obtained at step S30, and of ΔHi, ΔSi, and ΔVi which are obtained at step S31. At step S33, DHi, DSi, and DVi are added, and similarity Ri (i=1, 2, 3, , n: n is the total number of the stored images) is defined by the negative value of the result of the addition (-(DHi+DSi+DVi)). Likewise, similarity Ri(i=1,2,3,...,n) of the each stored image are calculated. In a case where a plurality of portion are designated, the similarity Rij (i=1,2,3,...,n:j=1,2,3,...,m: m is a total number of portion.) are calculated by each image. Then a representative degree of similarity of each image is defined by, for instance, the total value of the degrees of the similarity relating the images, namely, the result of addition Ri=Ri1+Ri2+...+Rim. Next, the process to decide the contribution factor of step S30 is described below in detail with reference to a flowchart in Fig. 6. The flowchart shows the process in the contribution factors deciding unit 60. Note that each contribution factor of the three attributes is denoted by Rh, Rs and Rv and each of the factors ranges from 0.0 to 1.0. Further, the normalized values of the three attributes, that is the normalized contribution factor, are denoted by RH, RS, and RV. Values of normalized contribution factors, which are outputted from the contribution factors deciding unit 60, are RH, RS, and RV which are obtained after the normalization process described below. At step 520, the color information of the sample image which is extracted at step S2 in Fig. 2 is inputted into the contribution factors deciding unit 60 from the sample image input unit 30 via the control unit 80. At step S21, out of the color information of the sample image which is read at step 520, whether or not luminance V1 is low, more specifically, whether or not the value of the luminance V1 is over predetermined threshold Tv, is examined. If the value of luminance V1 is smaller than the threshold Tv, the process proceeds to step S26, and contribution factor of hue H, chromaticity S, and luminance V are assigned as Rh=0, Rs=0, and Rv=1, respectively. In other words, the low luminance means the low contribution factor of chromaticity. Then the process moves to step S24. At step S24, since the contribution factors, Rh, Rs, and Rv are decided, these three values are normalized to obtain the normalized contribution factors RH, RS, and RV. Following are the equations for normalizing calculation. RH = Rh/(Rh+Rs+Rv) RS = Rs/(Rh+Rs+Rv) RV = Rv/(Rh+Rs+Rv) Next, at step S22, whether or not chromaticity S1 is low, more specifically, whether or not the value of the chromaticity S1 is over predetermined threshold Ts, is examined. If the value of chromaticity S1 is smaller than the threshold Ts, the process proceeds to step S27, and contribution factor of hue H, chromaticity S, and luminance V are assigned as Rh=0, Rs=1, and Rv=1, respectively. Then the process moves to step S24. Whereas, if the value of chromaticity S1 is larger than the threshold Ts, the process proceeds to step S23, and contribution factors of hue H, chromaticity S, and luminance V are assigned as Rh=1, Rs=0, and Rv=0, respectively. Then the process moves to step S24 to perform a normalizing procedure. Thus, the normalized contribution factors of the three attributes, RH, RS, and RV are decided by following the above described procedure. As explained above, the desired image is searched out of the stored images in accordance with the color information of the sample image made by the operator. Note that as an example of a sample image input means, the image can be drawn on paper or the like, and read by a scanner instead of drawing on a display. Further, various ways to designate a color of a portion during the process of making a sample image follow: 1. Choose a color out of limited colors which are displayed on a screen, 2. Designate a color by using color chips, and input a discrimination number of the designated color chip, 3. Input values of color data, such as RGB and HSV, in the form of number. Further, in a case where RGB color data is used as the sample image data, the RGB color data needs to be converted into the HSV space data, since the image data in the image storage unit 20 is HSV space data. This conversion from the RGB data to the HSV data is performed by using the following conversion algorithm. The conversion algorithm are fully explained in Fundamentals of Interactive Computer Graphic (J.D.Foley, A.Vandam, pp613-620). Note that in the first embodiment the HSV color space is employed as a color perception space which is defined by hue, chromaticity, and luminance. However, needless to say, it is possible to employ other color perception spaces, such as a Munsell color space. As explained above, according to this embodiment, it is easier for an operator to search for a desired image by improving the quality of the search using color information when searching for the similar images to the desired image out of an image database by using color information. (Second Embodiment) As a method for deciding contribution factors of the three attributes of color data in the first embodiment, thresholds Tv and Ts are predetermined to obtain the contribution factors. There is another method to determine the contribution factor by using predetermined membership functions. In a second embodiment, another example of practice of the contribution factors deciding unit 60 is described. Figs. 7 and 8 are examples of shapes of membership functions of luminance and chromaticity, respectively. In Fig. 7, a horizontal axis shows the value of luminance, and a vertical axis shows the membership grade of the corresponding luminance. Similarly, in Fig. 8, a horizontal axis shows the value of chromaticity, and a vertical axis shows membership grade of the corresponding chromaticity. The membership functions in Fig. 7 illustrates a membership function 101 which indicates that the luminance is low, and a membership function 100 which indicates that the luminance is not low. Likewise, the membership functions in Fig. 8 illustrates two membership functions 103 and 102, which indicates that the chromaticity is low, and that the chromaticity is not low, respectively. The membership functions 101 and 100 are denoted by M1v(i) and Mhv(i), respectively, hereinafter. Regarding i in the parenthesis, it indicates luminance. Further, M1v(i) and Mhv(i) are the membership grades relating to the luminance i. In the same manner, the membership functions 103 and 102 are denoted by M1s(j) and Mhs(j), respectively, hereinafter. Regarding j in the parenthesis, it indicates chromaticity. Therefore, M1s(j) and Mhs(j) are the membership grades relating to the chromaticity j. The process for deciding contribution factors of the three attributes of color data is explained below with reference to Figs. 9 and 10. Here, contribution factors (i.e., Rh, Rs and Rv) of color data X(H1, S1, V1) of a sample image illustrated by using the HSV color space are calculated by using membership functions. Followings are processes at each step. At step 540, color information of a sample image (H1, S1, V1) which is extracted at step S2 in Fig. 2 is inputted into the contribution factors deciding unit 60 from the sample image input unit 30 via the control unit 80. At step 541, membership grades, M1v(V1) and Mhv(V1) which correspond to luminance V1, are obtained by using the membership functions 101 and 100 in Fig. 7. At step S42, membership grades, M1s(S1) and Mhv(S1) corresponding to chromaticity S1, are obtained by referring to the membership functions 103 and 102 in Fig. 8. At step S43, the contribution factors, C1(x), C2(x), and C3(x) corresponding to three cases of the color data x are obtained based on the respective membership grades which are obtained at steps S41 and S42, where the three cases are case 1 (low luminance V), case 2 (high luminance V and low chromaticity S), and case 3 (high luminance V and high chromaticity S). The values of C1(x), C2(x) and C3(x) are given by the following equations. C1(X) = M1v(V1) C2 (X) = min(Mhv(V1), M1s(S1)) C3 (X) = min(Mhv(V1), Mhs(S1)) At step S44, the contribution factors of the three attributes of colors, Rh, Rs and Rv are obtained by performing matrix operation represented by S44 in Fig. 10. At step S45, these three values are normalized, and the normalized contribution factors RH, RS, and RV are obtained. The normalized contribution factors are given by the following equations. RH = Rh/(Rh+Rs+Rv) RS = Rs/(Rh+Rs+Rv) RV = Rv/(Rh+Rs+Rv) The present invention can be applied to a system constituted by a plurality of devices, or to an apparatus comprising a single device. Furthermore, the invention is applicable also to a case where the object of the invention is attained by supplying a program to a system or apparatus. As described above, according to the embodiment, the contribution factor of image color component of better quality is obtained by using the membership functions, thereby the better performance of image search is accomplished. Furthermore, according to the embodiment the quality of search using color information is improved when an image is searched from the image database. As many apparently widely different embodiments of the present invention can be made without departing from the scope thereof, it is to be understood that the invention is not limited to the specific embodiments thereof.

Claims: An image searching method for retrieving a desired image from an image database storing images comprising color information, the method comprising: a step (S1;S11) of receiving input information about a sample image for retrieving the desired image, the information designating the colour of at least one portion of the sample image; a generating step (S2) of generating colour information of the sample image, the information comprising a plurality of colour component values (H 1 , S 1 , V 1 ) corresponding to attributes of a perceptual colour space; and a similarity calculation step (S3-S4, S30-S33) of calculating a respective similarity (R i ) between the sample image and each image stored in the database, the calculation step comprising a calculation of similarity between the color component values (H 1 ,S 1 ,V 1 ) of the sample image and the color component values (H 2i , S 2i , V 2i ) of each image (i) stored in the database; characterised by further comprising a contribution factor calculation step (S20-23, S26, S27) of calculating for each of the plurality of color components a respective contribution factor (Rh,Rs,Ry; C1,C2,C3) as a function of the plurality of color components values of the sample image; a contribution rate calculating step of calculating (S30) for each of the plurality of colour components a respective contribution rate (RH, RS, RV) by normalizing (S24) the calculated contribution factors; and wherein the similarity calculation step calculates the respective similarity for each image stored in the database taking into account the corresponding contribution rates such that the rate at which each color component of the perceptual color space contributes to the similarity calculation is a function of the color component values in the sample image. A method as claimed in claim 1 further comprising: a displaying step (S6) of displaying images from the database as a searching result based on the similarity calculated at said similarity calculating step. A method as claimed in claim 2, wherein, at said displaying step, the images from the database are displayed in order of the similarity (S5,S6). A method as claimed in any preceding claim, wherein, at said input information receiving step, the color information is obtained by designating a color from a plurality of colors displayed on a display screen (S11, Fig. 4). A method as claimed in any preceding claim, wherein the plurality of color components include hue, chromaticity, and luminance. A method as claimed in claim 5, wherein, at said contribution factor calculation step, the contribution factor corresponding to each color component value is calculated based on a comparison (S21,S22) between the plurality of color component values and respective predetermined thresholds (T v ,T s ). The method according to claim 5, wherein, at said contribution factor calculation step, if the luminance (V 1 ) of said color information is lower than a predetermined threshold (T v ), then a contribution factor (R v ) corresponding to the luminance is set (S21,S26) to a higher level than the contribution factors for hue and chromaticity respectively, and wherein if the luminance is higher than said threshold and if the chromaticity of the color information is higher than a respective predetermined threshold (T s ), then a contribution factor (R h ) corresponding to hue is set (S22, S23) to be a higher level than the contribution factors for luminance and chromaticity respectively, further wherein if the luminance of said color information is higher than said predetermined threshold (T v ) and the chromaticity of said color information is lower than the respective predetermined threshold (T s ), then the contribution factor (R s ) of the chromaticity of the color information and the contribution factor (R v ) of the luminance of the color information are set (S22,S27) to respective levels which are higher than the contribution factor for hue. A method as claimed in claim 5, wherein, at said contribution factor calculation step, the calculation is performed based on a plurality of membership functions (100, 101, 102, 103; Figs. 7-10) which define membership grades (C1,C2,C3) of chromaticity and luminance of the color information. A method as claimed in any preceding claim wherein the step of receiving input information further comprises receiving (S10) portion information representative of a portion of the image to which the color information relates and wherein the generating step uses both the color information and the portion information to generate the sample image. A method as claimed in claim 9, wherein, at said step of receiving input information, the portion information indicates a portion representative of an object image and wherein the portion is filled (S12) by a color defined by the color information. A method as claimed in claim 10 wherein the step of receiving input information is responsive to input by an operator by drawing said object image on a display using a pointing device to define said portion information. A method as claimed in claim 10 wherein the step of receiving input information is responsive to the input of said portion information by operation of a scanner. Image searching apparatus for retrieving a desired image from a database including color information of stored images, the apparatus comprising: input means (30, 31) operable to input information about a sample image for retrieving the desired image, the information designating the colour of at least one portion of the sample image; generating means (50) operable to generate colour information of the sample image, the information comprising a plurality of colour component values (HI, SI, VI) corresponding to attributes of a perceptual colour space; similarity calculation means (70) operable to perform a calculation of a respective similarity (R i ) between the sample image and each image stored in the database, the calculation comprising a calculation of similarity between the color component values(H 1 ,S 1 ,V 1 ) of the sample image and the color component values (H 2i , S 2i , V 2i ) of each image (i) stored in the database; characterised by further comprising contribution factor calculating means (60) operable to calculate for each of the plurality of color components a respective contribution factor (Rh,Rs,Rv; C1,C2,C3) as a function of the plurality of color component values of the sample image; contribution rate calculating means (60) operable to calculate for each of the plurality of color components a respective contribution rate (RH, RS, RV) by normalizing (S24) the calculated contribution factors; and wherein the similarity calculating means is operable to calculate the similarity between the sample image and each image stored in the database taking into account the corresponding contribution rates such that the rate at which each color component of the perceived color space contributes to the similarity calculation is a function of the color component values in the sample image. Apparatus as claimed in claim 13 further comprising: a display unit (90) operable to display images from the database as a searching result based on the similarity calculated by said similarity calculating means. Apparatus as claimed in claim 14, wherein said display unit is operable to display the images from the database in order of the similarity. Apparatus as claimed in any of claims 13 to 15 comprising designating means (31) operable to designate the color information by designating a color from a plurality of colors displayed on a display screen of said display unit. Apparatus as claimed in any of claims 13 to 16 wherein the plurality of color components include hue, chromaticity, and luminance. Apparatus as claimed in claim 17, wherein said contribution factor calculating means is operable to calculate the contribution factor corresponding to each color component of color information based on a comparison (S21,S22) between the plurality of color component values and respective predetermined thresholds (T v ,T s ). Apparatus as claimed in claim 17 wherein the contribution factor calculation means is operable such that, if the luminance (V 1 ) of said color information is lower than a predetermined threshold (T v ), then a contribution factor (R v ) corresponding to the luminance is set (S21,S26) to a higher level than the contribution factors for hue and chromaticity respectively, and wherein, if the luminance is higher than said threshold and if the chromaticity of the color information is higher than a respective predetermined threshold (T s ), then a contribution factor (R h ) corresponding to hue is set to be a higher level than the contribution factors for luminance and chromaticity respectively (S22, S23), further wherein, if the luminance of said color information is higher than said predetermined threshold (T v ) and the chromaticity of said color information is lower than the respective predetermined threshold (T s ), then the contribution factor (R s ) of the chromaticity of the color information and the contribution factor (R v ) of the luminance of the color information are set (S22,S27) to respective levels which are higher than the contribution factor for hue. Apparatus as claimed in claim 17, wherein said contribution factor calculation means is operable to perform calculation based on a plurality of membership functions (100, 101, 102, 103; Figs. 7-10) which define membership grades (C1,C2,C3) of chromaticity and luminance of the color information. Apparatus as claimed in any of claims 13 to 20 wherein the input means is further operable to input portion information representative of a portion of the image to which the color information relates and wherein the generating means is operable to use both the color information and the portion information to generate the sample image. Apparatus as claimed in claim 21, wherein said input means is operable such that the portion information indicates a portion representative of an object image and is filled by a color defined by the color information (S10,S11). Apparatus as claimed in claim 22 wherein the input means comprises a pointing device (31) responsive to input by an operator by drawing said object image on a display using the pointing device to define said portion information. Apparatus as claimed in claim 22 wherein the input means comprises a scanner operable to input said portion information. A computer program comprising processor implementable instructions for controlling an image searching apparatus to carry out all of the steps of a method as claimed in any of claims 1 to 12.

Inventors: BANNAI YUICHI, TSUJIMURA KAZUKO, BANNAI, YUICHI, TSUJIMURA, KAZUKO, BANNAI, YUICHI, C/O CANON K. K., TSUJIMURA, KAZUKO, C/O CANON K. K.
Applicants: CANON KK, CANON KABUSHIKI KAISHA
Application year: 1994
Main classification: G06F17
IPC codes: G06T7, G06F17